A systematic review of the use of artificial intelligence in mental health–based diabetes care: Current applications and future directions

Abstract

AIMS: To map and systematise existing research on the use of artificial intelligence (AI) in mental health-based diabetes care contexts, identify trends and potential gaps in the literature, examine methodological limitations and highlight future research directions. METHODS: The review adhered to PRISMA guidelines and was pre-registered on PROSPERO (CRD420251167053). Comprehensive searches were conducted across nine databases, including MEDLINE, EMBASE, PsycINFO and IEEE Xplore, using a Boolean strategy that combined terms related to diabetes, AI and clinical mental health. Inclusion criteria encompassed peer-reviewed, empirical, quantitative studies involving humans, diabetes contexts, mental health factors and AI-based methodologies. Screening and data extraction were performed independently by two reviewers. Forty-one studies ultimately met the inclusion criteria. RESULTS: Research on AI in mental health-based diabetes care contexts has grown substantially since 2020. Most studies employed observational (83%) and cross-sectional (56%) designs, focused on assessment rather than intervention (88%) and targeted depression (56%). Supervised learning algorithms were most frequently used (83%); however, deep learning models achieved the highest performance. Despite technological advances, no temporal improvement in algorithmic performance was observed. Methodological limitations included limited diversity in samples and outcomes, minimal use of prospective experimental and randomised controlled trial-based designs and overreliance on supervised learning algorithms. CONCLUSIONS: AI shows promise in addressing mental health needs in diabetes care. However, current research is narrow in scope and lacks methodological rigour in some respects. Future studies might profitably prioritise diverse populations, prospective designs, interpretability and clinical utility to enable safe, effective and equitable integration of AI into person-centred diabetes care.

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